Molecular biology and evolution. Can genes explain biological complexity?

Divergent vertebral formulae shape the evolution of axial complexity in mammals

Complexity, defined as the number of parts and their degree of differentiation, is a poorly explored aspect of macroevolutionary dynamics. The maximum anatomical complexity of organisms has undoubtedly increased through evolutionary time. However, it is unclear whether this increase is a purely diffusive process or whether it is at least partly driven, occurring in parallel in most or many lineages and with increases in the minima as well as the means. Highly differentiated and serially repeated structures, such as vertebrae, are useful systems with which to investigate these patterns. We focus on the serial differentiation of the vertebral column in 1,136 extant mammal species, using two indices that quantify complexity as the numerical richness and proportional distribution of vertebrae across presacral regions and a third expressing the ratio between thoracic and lumbar vertebrae. We address three questions. First, we ask whether the distribution of complexity values in major mammal groups is similar or whether clades have specific signatures associated with their ecology. Second, we ask whether changes in complexity throughout the phylogeny are biased towards increases and whether there is evidence of driven trends. Third, we ask whether evolutionary shifts in complexity depart from a uniform Brownian motion model. Vertebral counts, but not complexity indices, differ significantly between major groups and exhibit greater within-group variation than recognized hitherto. We find strong evidence of a trend towards increasing complexity, where higher values propagate further increases in descendant lineages. Several increases are inferred to have coincided with major ecological or environmental shifts. We find support for multiple-rate models of evolution for all complexity metrics, suggesting that increases in complexity occurred in stepwise shifts, with evidence for widespread episodes of recent rapid divergence. Different subclades evolve more complex vertebral columns in different configurations and probably under different selective pressures and constraints, with widespread convergence on the same formulae. Further work should therefore focus on the ecological relevance of differences in complexity and a more detailed understanding of historical patterns.

The Axes of Life: A roadmap for understanding dynamic multiscale systems

The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast data sets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists.

Extreme Antagonism Arising from Gene-Environment Interactions

Antagonistic interactions in biological systems, which occur when one perturbation blunts the effect of another, are typically interpreted as evidence that the two perturbations impact the same cellular pathway or function. Yet, this interpretation ignores extreme antagonistic interactions wherein an otherwise deleterious perturbation compensates for the function lost because of a prior perturbation. Here, we report on gene-environment interactions involving genetic mutations that are deleterious in a permissive environment but beneficial in a specific environment that restricts growth. These extreme antagonistic interactions constitute gene-environment analogs of synthetic rescues previously observed for gene-gene interactions. Our approach uses two independent adaptive evolution steps to address the lack of experimental methods to systematically identify such extreme interactions. We apply the approach to Escherichia coli by successively adapting it to defined glucose media without and with the antibiotic rifampicin. The approach identified multiple mutations that are beneficial in the presence of rifampicin and deleterious in its absence. The analysis of transcription shows that the antagonistic adaptive mutations repress a stringent response-like transcriptional program, whereas nonantagonistic mutations have an opposite transcriptional profile. Our approach represents a step toward the systematic characterization of extreme antagonistic gene-drug interactions, which can be used to identify targets to select against antibiotic resistance.

Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series

The growing pace of environmental change has increased the need for large-scale monitoring of biodiversity. Declining intraspecific genetic variation is likely a critical factor in biodiversity loss, but is especially difficult to monitor: assessments of genetic variation are commonly based on measuring allele pools, which requires sampling of individuals and extensive sample processing, limiting spatial coverage. Alternatively, imaging spectroscopy data from remote platforms may hold the potential to reveal genetic structure of populations. In this study, we investigated how differences detected in an airborne imaging spectroscopy time series correspond to genetic variation within a population of Fagus sylvatica under natural conditions.We used multi-annual APEX (Airborne Prism Experiment) imaging spectrometer data from a temperate forest located in the Swiss midlands (Laegern, 47°28'N, 8°21'E), along with microsatellite data from F. sylvatica individuals collected at the site. We identified variation in foliar reflectance independent of annual and seasonal changes which we hypothesize is more likely to correspond to stable genetic differences. We established a direct connection between the spectroscopy and genetics data by using partial least squares (PLS) regression to predict the probability of belonging to a genetic cluster from spectral data.We achieved the best genetic structure prediction by using derivatives of reflectance and a subset of wavebands rather than full-analyzed spectra. Our model indicates that spectral regions related to leaf water content, phenols, pigments, and wax composition contribute most to the ability of this approach to predict genetic structure of F. sylvatica population in natural conditions.This study advances the use of airborne imaging spectroscopy to assess tree genetic diversity at canopy level under natural conditions, which could overcome current spatiotemporal limitations on monitoring, understanding, and preventing genetic biodiversity loss imposed by requirements for extensive in situ sampling.

SUMOylation in development and neurodegeneration

In essentially all eukaryotes, proteins can be modified by the attachment of small ubiquitin-related modifier (SUMO) proteins to lysine side chains to produce branched proteins. This process of 'SUMOylation' plays essential roles in plant and animal development by altering protein function in spatially and temporally controlled ways. In this Primer, we explain the process of SUMOylation and summarize how SUMOylation regulates a number of signal transduction pathways. Next, we discuss multiple roles of SUMOylation in the epigenetic control of transcription. In addition, we evaluate the role of SUMOylation in the etiology of neurodegenerative disorders, focusing on Parkinson's disease and cerebral ischemia. Finally, we discuss the possibility that SUMOylation may stimulate survival and neurogenesis of neuronal stem cells.

p62/SQSTM1: 'Jack of all trades' in health and cancer

p62 is a stress‐inducible protein able to change among binding partners, cellular localizations and form liquid droplet structures in a context‐dependent manner. This protein is mainly defined as a cargo receptor for selective autophagy, a process that allows the degradation of detrimental and unnecessary components through the lysosome. Besides this role, its ability to interact with multiple binding partners allows p62 to act as a main regulator of the activation of the Nrf2, mTORC1, and NF‐κB signaling pathways, linking p62 to the oxidative defense system, nutrient sensing, and inflammation, respectively. In the present review, we will present the molecular mechanisms behind the control p62 exerts over these pathways, their interconnection and how their deregulation contributes to cancer progression.

A two-level model for the role of complex and young genes in the formation of organism complexity and new insights into the relationship between evolution and development

Background

How genome complexity affects organismal phenotypic complexity is a fundamental question in evolutionary developmental biology. Previous studies proposed various contributing factors of genome complexity and tried to find the connection between genomic complexity and organism complexity. However, a general model to answer this question is lacking. Here, we introduce a ‘two-level’ model for the realization of genome complexity at phenotypic level.

Results

Five representative species across Protostomia and Deuterostomia were involved in this study. The intrinsic gene properties contributing to genome complexity were classified into two generalized groups: the complexity and age degree of both protein-coding and noncoding genes. We found that young genes tend to be simpler; however, the mid-age genes, rather than the oldest genes, show the highest proportion of high complexity. Complex genes tend to be utilized preferentially in each stage of embryonic development, with maximum representation during the late stage of organogenesis. This trend is mainly attributed to mid-age complex genes. In contrast, young genes tend to be expressed in specific spatiotemporal states. An obvious correlation between the time point of the change in over- and under-representation and the order of gene age was observed, which supports the funnel-like model of the conservation pattern of development. In addition, we found some probable causes for the seemingly contradictory ‘funnel-like’ or ‘hourglass’ model.

Conclusions

These results indicate that complex and young genes contribute to organismal complexity at two different levels: Complex genes contribute to the complexity of individual proteomes in certain states, whereas young genes contribute to the diversity of proteomes in different spatiotemporal states. This conclusion is valid across the five species investigated, indicating it is a conserved model across Protostomia and Deuterostomia. The results in this study also support ‘funnel-like model’ from a new viewpoint and explain why there are different evo–devo relation models.

Electronic supplementary material

The online version of this article (10.1186/s13227-018-0111-4) contains supplementary material, which is available to authorized users.

Demography and its effects on genomic variation in crop domestication

Over two thousand plant species have been modified morphologically through cultivation and human use. Here, we review three aspects of crop domestication that are currently undergoing marked revisions, due to analytical advancements and their application to whole genome resequencing (WGS) data. We begin by discussing the duration and demographic history of domestication. There has been debate as to whether domestication occurred quickly or slowly. The latter is tentatively supported both by fossil data and application of WGS data to sequentially Markovian coalescent methods that infer the history of effective population size. This history suggests the possibility of extended human impacts on domesticated lineages prior to their purposeful cultivation. We also make the point that demographic history matters, because it shapes patterns and levels of extant genetic diversity. We illustrate this point by discussing the evolutionary processes that contribute to the empirical observation that most crops examined to date have more putatively deleterious alleles than their wild relatives. These deleterious alleles may contribute to genetic load within crops and may be fitting targets for crop improvement. Finally, the same demographic factors are likely to shape the spectrum of structural variants (SVs) within crops. SVs are known to underlie many of the phenotypic changes associated with domestication and crop improvement, but we currently lack sufficient knowledge about the mechanisms that create SVs, their rates of origin, their population frequencies and their phenotypic effects.

An evolutionary perspective on the systems of adaptive immunity

We propose an evolutionary perspective to classify and characterize the diverse systems of adaptive immunity that have been discovered across all major domains of life. We put forward a new function-based classification according to the way information is acquired by the immune systems: Darwinian immunity (currently known from, but not necessarily limited to, vertebrates) relies on the Darwinian process of clonal selection to 'learn' by cumulative trial-and-error feedback; Lamarckian immunity uses templated targeting (guided adaptation) to internalize heritable information on potential threats; finally, shotgun immunity operates through somatic mechanisms of variable targeting without feedback. We argue that the origin of Darwinian (but not Lamarckian or shotgun) immunity represents a radical innovation in the evolution of individuality and complexity, and propose to add it to the list of major evolutionary transitions. While transitions to higher-level units entail the suppression of selection at lower levels, Darwinian immunity re-opens cell-level selection within the multicellular organism, under the control of mechanisms that direct, rather than suppress, cell-level evolution for the benefit of the individual. From a conceptual point of view, the origin of Darwinian immunity can be regarded as the most radical transition in the history of life, in which evolution by natural selection has literally re-invented itself. Furthermore, the combination of clonal selection and somatic receptor diversity enabled a transition from limited to practically unlimited capacity to store information about the antigenic environment. The origin of Darwinian immunity therefore comprises both a transition in individuality and the emergence of a new information system - the two hallmarks of major evolutionary transitions. Finally, we present an evolutionary scenario for the origin of Darwinian immunity in vertebrates. We propose a revival of the concept of the 'Big Bang' of vertebrate immunity, arguing that its origin involved a 'difficult' (i.e. low-probability) evolutionary transition that might have occurred only once, in a common ancestor of all vertebrates. In contrast to the original concept, we argue that the limiting innovation was not the generation of somatic diversity, but the regulatory circuitry needed for the safe operation of amplifiable immune responses with somatically acquired targeting. Regulatory complexity increased abruptly by genomic duplications at the root of the vertebrate lineage, creating a rare opportunity to establish such circuitry. We discuss the selection forces that might have acted at the origin of the transition, and in the subsequent stepwise evolution leading to the modern immune systems of extant vertebrates.

Engineering of a synthetic quadrastable gene network to approach Waddington landscape and cell fate determination

The process of cell fate determination has been depicted intuitively as cells travelling and resting on a rugged landscape, which has been probed by various theoretical studies. However, few studies have experimentally demonstrated how underlying gene regulatory networks shape the landscape and hence orchestrate cellular decision-making in the presence of both signal and noise. Here we tested different topologies and verified a synthetic gene circuit with mutual inhibition and auto-activations to be quadrastable, which enables direct study of quadruple cell fate determination on an engineered landscape. We show that cells indeed gravitate towards local minima and signal inductions dictate cell fates through modulating the shape of the multistable landscape. Experiments, guided by model predictions, reveal that sequential inductions generate distinct cell fates by changing landscape in sequence and hence navigating cells to different final states. This work provides a synthetic biology framework to approach cell fate determination and suggests a landscape-based explanation of fixed induction sequences for targeted differentiation.

DOI:http://dx.doi.org/10.7554/eLife.23702.001

Cells in animals use a process called differentiation to specialize into specific cell types such as skin cells and liver cells. Proteins called transcription factors drive particular steps in differentiation by controlling the activity of specific genes. Many transcription factors interact with each other to form complex networks that regulate gene activity to determine the fate of a cell and control the whole differentiation process. Some individual gene networks can program cells to become any one of several different cell fates, a feature known as multistability.

In the 1950s, a scientist called Conrad Waddington proposed the concept of an “epigenetic landscape” to describe how the fate of a cell is decided as an animal develops. The cell, depicted as a ball, rolls down a rugged landscape and has the option of taking several different routes. Each route will eventually lead to a distinct cell fate. As the ball moves down the hill, the choice of routes and final destinations becomes more limited. Theoretical approaches have been used to understand how gene regulatory networks shape the epigenetic landscape of an animal. However, few studies have experimentally tested the findings of the theoretical approaches and it is not clear how environmental inputs help to determine which path a cell will take.

Although bacteria cells do not generally specialize into particular cell types, bacteria cells can use multistability in transcription factor networks to switch between different behaviors or “states” in response to cues from the environment. Wu et al. used a bacterium called E. coli as a model to investigate how a gene network called MINPA from mammals, which is involved in differentiation and is believed to show multistability, can guide cells to adopt different states. The work combined experimental and mathematical approaches to design, construct and test an artificial version of the MINPA gene network in E. coli.

The experiments showed that MINPA could direct the cells to adopt four different stable states in which the cells produced fluorescent proteins of different colors. With the help of mathematical modeling, Wu et al. charted how the landscape of cell states changed when external chemical cues were applied. Exposing the cells to several cues in particular orders guided the cells to different final states.

The findings of Wu et al. shed new light on how the fate of a cell is determined and provide a theoretical framework for understanding the complex networks that control cell differentiation. This could help develop new ways of directing cell fate that could ultimately be used to generate cells to treat human diseases.

DOI:http://dx.doi.org/10.7554/eLife.23702.002

Evolution by gene loss

The recent increase in genomic data is revealing an unexpected perspective of gene loss as a pervasive source of genetic variation that can cause adaptive phenotypic diversity. This novel perspective of gene loss is raising new fundamental questions. How relevant has gene loss been in the divergence of phyla? How do genes change from being essential to dispensable and finally to being lost? Is gene loss mostly neutral, or can it be an effective way of adaptation? These questions are addressed, and insights are discussed from genomic studies of gene loss in populations and their relevance in evolutionary biology and biomedicine.

A morphospace for synthetic organs and organoids: the possible and the actual

Efforts in evolutionary developmental biology have shed light on how organs are developed and why evolution has selected some structures instead of others. These advances in the understanding of organogenesis along with the most recent techniques of organotypic cultures, tissue bioprinting and synthetic biology provide the tools to hack the physical and genetic constraints in organ development, thus opening new avenues for research in the form of completely designed or merely altered settings. Here we propose a unifying framework that connects the concept of morphospace (i.e. the space of possible structures) with synthetic biology and tissue engineering. We aim for a synthesis that incorporates our understanding of both evolutionary and architectural constraints and can be used as a guide for exploring alternative design principles to build artificial organs and organoids. We present a three-dimensional morphospace incorporating three key features associated to organ and organoid complexity. The axes of this space include the degree of complexity introduced by developmental mechanisms required to build the structure, its potential to store and react to information and the underlying physical state. We suggest that a large fraction of this space is empty, and that the void might offer clues for alternative ways of designing and even inventing new organs.

The dynamic epitranscriptome: A to I editing modulates genetic information

Adenosine to inosine editing (A to I editing) is a cotranscriptional process that contributes to transcriptome complexity by deamination of adenosines to inosines. Initially, the impact of A to I editing has been described for coding targets in the nervous system. Here, A to I editing leads to recoding and changes of single amino acids since inosine is normally interpreted as guanosine by cellular machines. However, more recently, new roles for A to I editing have emerged: Editing was shown to influence splicing and is found massively in Alu elements. Moreover, A to I editing is required to modulate innate immunity. We summarize the multiple ways in which A to I editing generates transcriptome variability and highlight recent findings in the field.

Different types of secondary information in the genetic code

Whole-genome and functional analyses suggest a wealth of secondary or auxiliary genetic information (AGI) within the redundancy component of the genetic code. Although there are multiple aspects of biased codon use, we focus on two types of auxiliary information: codon-specific translational pauses that can be used by particular proteins toward their unique folding and biased codon patterns shared by groups of functionally related mRNAs with coordinate regulation. AGI is important to genetics in general and to human disease; here, we consider influences of its three major components, biased codon use itself, variations in the tRNAome, and anticodon modifications that distinguish synonymous decoding. AGI is plastic and can be used by different species to different extents, with tissue-specificity and in stress responses. Because AGI is species-specific, it is important to consider codon-sensitive experiments when using heterologous systems; for this we focus on the tRNA anticodon loop modification enzyme, CDKAL1, and its link to type 2 diabetes. Newly uncovered tRNAome variability among humans suggests roles in penetrance and as a genetic modifier and disease modifier. Development of experimental and bioinformatics methods are needed to uncover additional means of auxiliary genetic information.

An estimation of the number of cells in the human body

BACKGROUND

All living organisms are made of individual and identifiable cells, whose number, together with their size and type, ultimately defines the structure and functions of an organism. While the total cell number of lower organisms is often known, it has not yet been defined in higher organisms. In particular, the reported total cell number of a human being ranges between 10(12) and 10(16) and it is widely mentioned without a proper reference.

AIM

To study and discuss the theoretical issue of the total number of cells that compose the standard human adult organism.

SUBJECTS AND METHODS

A systematic calculation of the total cell number of the whole human body and of the single organs was carried out using bibliographical and/or mathematical approaches.

RESULTS

A current estimation of human total cell number calculated for a variety of organs and cell types is presented. These partial data correspond to a total number of 3.72 × 10(13).

CONCLUSIONS

Knowing the total cell number of the human body as well as of individual organs is important from a cultural, biological, medical and comparative modelling point of view. The presented cell count could be a starting point for a common effort to complete the total calculation.

Nonclinical and clinical Enterococcus faecium strains, but not Enterococcus faecalis strains, have distinct structural and functional genomic features

Certain strains of Enterococcus faecium and Enterococcus faecalis contribute beneficially to animal health and food production, while others are associated with nosocomial infections. To determine whether there are structural and functional genomic features that are distinct between nonclinical (NC) and clinical (CL) strains of those species, we analyzed the genomes of 31 E. faecium and 38 E. faecalis strains. Hierarchical clustering of 7,017 orthologs found in the E. faecium pangenome revealed that NC strains clustered into two clades and are distinct from CL strains. NC E. faecium genomes are significantly smaller than CL genomes, and this difference was partly explained by significantly fewer mobile genetic elements (ME), virulence factors (VF), and antibiotic resistance (AR) genes. E. faecium ortholog comparisons identified 68 and 153 genes that are enriched for NC and CL strains, respectively. Proximity analysis showed that CL-enriched loci, and not NC-enriched loci, are more frequently colocalized on the genome with ME. In CL genomes, AR genes are also colocalized with ME, and VF are more frequently associated with CL-enriched loci. Genes in 23 functional groups are also differentially enriched between NC and CL E. faecium genomes. In contrast, differences were not observed between NC and CL E. faecalis genomes despite their having larger genomes than E. faecium. Our findings show that unlike E. faecalis, NC and CL E. faecium strains are equipped with distinct structural and functional genomic features indicative of adaptation to different environments.

Density peaks of paralog pairs in human and mouse genomes

Paralog gene trees, which reflect the increase of genomic complexity in the evolution, can be complicated and ambiguous. A simpler complementary approach is analysis of density distribution of paralog pairs. It can reveal general features of genome evolution, which may be hidden in the forest of gene trees. It is known that distribution of human paralog pairs along the axis of protein divergence between pair members forms two main peaks. Here I show that there are three main peaks in the mouse genome. Thus, the multimodality of paralog pair distribution seems to be a fundamental feature of mammalian genomes. Despite the great diversity of domains presented in small amounts or in multidomain architectures with a few predominant domains, both in human and mouse the first peak consists mostly of gene pairs with zinc finger domains or olfactory receptor domain. In the mouse the olfactory receptor predominates, which stipulates the three-peak distribution (since in the olfactory receptors the second peak is closer to the first peak than in other genes). The mammalian-wide zinc finger orthologs are biased towards the second peak. Thus, the marsupial orthologs are nearly absent in the first peak of human and mouse. The gene pairs in the first peak show a lower ratio of nonsynonymous to synonymous substitutions, which suggests that their evolution is more constrained. The plausible explanation is that they are in subfunctionalization state (partition of initial function of ancestral gene), whereas the second peak contains gene pairs that are already in neofunctionalization state (acquiring of novel functions). These data suggest that the adaptive radiation of mammals was accompanied by a burst of duplication of zinc finger genes, which are located in the first (most recent) peak of paralog pairs.

Studying protein-protein interaction networks: a systems view on diseases

In order to better understand several cellular processes, it is helpful to study how various components make up the system. This systems perspective is supported by several modelling tools including network analysis. Networks of protein-protein interactions (PPI networks) offer a way to depict, visualize and quantify the functioning and relative importance of particular proteins in cell function. The toolkit of network analysis ranges from the local indices describing individual proteins (as network nodes) to global indicators of system architecture, describing the total interaction system (as the whole network). We briefly introduce some of these network indices and present a case study where the connectedness and potential functional relationships between certain disease proteins are inferred. We argue that network analysis can be used, in general, to improve databases, to infer novel functions, to quantify positional importance and to support predictions in pathogenesis studies. The systems perspective and network analysis can be of particular importance in studying diseases with complex molecular processes.

A comparison of methods sensitive to interactions with small main effects

Numerous genetic variants have been successfully identified for complex traits, yet these genetic factors only account for a modest portion of the predicted variance due to genetic factors. This has led to increased interest in other approaches to account for the "missing" genetic contributions to phenotype, including joint gene-gene or gene-environment analysis. A variety of methods for such analysis have been advocated. However, they have seldom been compared systematically. To facilitate such comparisons, the developers of the multifactor dimensionality reduction (MDR) simulated 100 data replicates for each of 96 two-locus models displaying negligible marginal effects from either locus (16 variations on each of six basic genetic models). The genetic models, based on a dichotomous phenotype, had varying minor allele frequencies and from two to eight distinct risk levels associated with genotype. The basic models were modified to include "noise" from combinations of missing data, genotyping error, genetic heterogeneity, and phenocopies. This study compares the performance of three methods designed to be sensitive to joint effects (MDR, support vector machines (SVMs), and the restricted partition method (RPM)) on these simulated data. In these tests, the RPM consistently outperformed the other two methods for each of the six classes of genetic models. In contrast, the comparison between other two methods had mixed results. The MDR outperformed the SVM when the true model had only a few, well-separated risk classes; while the SVM outperformed the MDR on more complicated models. Of these methods, only MDR has a well-developed user interface.

General trends in the utilization of structural factors contributing to biological complexity

During evolution, proteins containing newly emerged domains and the increasing proportion of multidomain proteins in the full genome-encoded proteome (GEP) have substantially contributed to increasing biological complexity. However, it is not known how these two potential structural factors are preferentially utilized at given physiological states. Here, we classified proteins according to domain number and domain age and explored the general trends across species for the utilization of proteins from GEP to various certain-state proteomes (CSPs, i.e., all the proteins expressed at certain physiological states). We found that multidomain proteins or only older domain-containing proteins are significantly overrepresented in CSPs compared with GEP, which is a trend that is stronger in multicellular organisms than in unicellular organisms. Interestingly, the strengths of overrepresentation decreased during evolution of multicellular eukaryotes. When comparing across CSPs, we found that multidomain proteins are more overrepresented in complex tissues than in simpler ones, whereas no difference among proteins with domains of different ages is evident between complex and simple tissues. Thus, biological complexity under certain conditions is more significantly realized by diverse domain organization than by the emergence of new types of domain. In addition, we found that multidomain or only older domain-containing proteins tend to evolve slowly and generally are under stronger purifying selection, which may partly result from their general overrepresentation trends in CSPs.

The emergence of complexity and restricted pleiotropy in adapting networks

Background

The emergence of organismal complexity has been a difficult subject for researchers because it is not readily amenable to investigation by experimental approaches. Complexity has a myriad of untested definitions and our understanding of its evolution comes primarily from static snapshots gleaned from organisms ranked on an intuitive scale. Fisher's geometric model of adaptation, which defines complexity as the number of phenotypes an organism exposes to natural selection, provides a theoretical framework to study complexity. Yet investigations of this model reveal phenotypic complexity as costly and therefore unlikely to emerge.

Results

We have developed a computational approach to study the emergence of complexity by subjecting neural networks to adaptive evolution in environments exacting different levels of demands. We monitored complexity by a variety of metrics. Top down metrics derived from Fisher's geometric model correlated better with the environmental demands than bottom up ones such as network size. Phenotypic complexity was found to increase towards an environment-dependent level through the emergence of restricted pleiotropy. Such pleiotropy, which confined the action of mutations to only a subset of traits, better tuned phenotypes in challenging environments. However, restricted pleiotropy also came at a cost in the form of a higher genetic load, as it required the maintenance by natural selection of more independent traits. Consequently, networks of different sizes converged in complexity when facing similar environment.

Conclusions

Phenotypic complexity evolved as a function of the demands of the selective pressures, rather than the physical properties of the network architecture, such as functional size. Our results show that complexity may be more predictable, and understandable, if analyzed from the perspective of the integrated task the organism performs, rather than the physical architecture used to accomplish such tasks. Thus, top down metrics emphasizing selection may be better for describing biological complexity than bottom up ones representing size and other physical attributes.

Inferring pleiotropy by network analysis: linked diseases in the human PPI network

Background

Earlier, we identified proteins connecting different disease proteins in the human protein-protein interaction network and quantified their mediator role. An analysis of the networks of these mediators shows that proteins connecting heart disease and diabetes largely overlap with the ones connecting heart disease and obesity.

Results

We quantified their overlap, and based on the identified topological patterns, we inferred the structural disease-relatedness of several proteins. Literature data provide a functional look of them, well supporting our findings. For example, the inferred structurally important role of the PDZ domain-containing protein GIPC1 in diabetes is supported despite the lack of this information in the Online Mendelian Inheritance in Man database. Several key mediator proteins identified here clearly has pleiotropic effects, supported by ample evidence for their general but always of only secondary importance.

Conclusions

We suggest that studying central nodes in mediator networks may contribute to better understanding and quantifying pleiotropy. Network analysis provides potentially useful tools here, as well as helps in improving databases.

The plausible reason why the length of 5' untranslated region is unrelated to organismal complexity

BACKGROUND

Organismal complexity is suggested to increase with the complexity of transcriptional and translational regulations. Supporting this notion is a recent study that demonstrated a higher level of tissue-specific gene expression in human than in mouse. However, whether this correlation can be extended beyond mammals remains unclear. In addition, 5' untranslated regions (5'UTRs), which have undergone stochastic elongation during evolution and potentially included an increased number of regulatory elements, may have played an important role in the emergence of organismal complexity. Although the lack of correlation between 5'UTR length and organismal complexity has been proposed, the underlying mechanisms remain unexplored.

RESULTS

In this study, we select the number of cell types as the measurement of organismal complexity and examine the correlation between (1) organismal complexity and transcriptional regulatory complexity; and (2) organismal complexity and 5'UTR length by comparing the 5'UTRs and multiple-tissue expression profiles of human (Homo sapiens), mouse (Mus musculus), and fruit fly (Drosophila melanogaster). The transcriptional regulatory complexity is measured by using the tissue specificity of gene expression and the ratio of non-constitutively expressed to constitutively expressed genes. We demonstrate that, whereas correlation (1) holds well in the three-way comparison, correlation (2) is not true. Results from a larger dataset that includes more than 15 species, ranging from yeast to human, also reject correlation (2). The reason for the failure of correlation (2) may be ascribed to: Firstly, longer 5'UTRs do not contribute to increased tissue specificity of gene expression. Secondly, the increased numbers of common translational regulatory elements in longer 5'UTRs do not lead to increased organismal complexity.

CONCLUSIONS

Our study has extended the evidence base for the correlation between organismal complexity and transcriptional regulatory complexity from mammals to fruit fly, the representative model organism of invertebrates. Furthermore, our results suggest that the elongation of 5'UTRs alone can not lead to the increase in regulatory complexity or the emergence of organismal complexity.

Uncovering hidden variance: pair-wise SNP analysis accounts for additional variance in nicotine dependence

Results from genome-wide association studies of complex traits account for only a modest proportion of the trait variance predicted to be due to genetics. We hypothesize that joint analysis of polymorphisms may account for more variance. We evaluated this hypothesis on a case-control smoking phenotype by examining pairs of nicotinic receptor single-nucleotide polymorphisms (SNPs) using the Restricted Partition Method (RPM) on data from the Collaborative Genetic Study of Nicotine Dependence (COGEND). We found evidence of joint effects that increase explained variance. Four signals identified in COGEND were testable in independent American Cancer Society (ACS) data, and three of the four signals replicated. Our results highlight two important lessons: joint effects that increase the explained variance are not limited to loci displaying substantial main effects, and joint effects need not display a significant interaction term in a logistic regression model. These results suggest that the joint analyses of variants may indeed account for part of the genetic variance left unexplained by single SNP analyses. Methodologies that limit analyses of joint effects to variants that demonstrate association in single SNP analyses, or require a significant interaction term, will likely miss important joint effects.

Functional impact of transposable elements using bioinformatic analysis and a comparative genomic approach

A dual coding event, which is the translation of different isoforms from a single gene, is one of the special patterns among the alternative splicing events. This is an important mechanism for the regulation of protein diversity in human and mouse genomes. Although the regulation for dual coding events has been characterized in a few genes, the individual mechanism remains unclear. Numerous studies have described the exonization of transposable elements, which is the splicing mediated insertion of transposable element sequence fragments into mature mRNAs. Therefore, in this study, we investigated the number of transposable element (TE)-derived dual coding genes in human, chimpanzee and mouse genomes. TE fusion exons appeared in the dual coding regions of 309 human genes. Functional protein domain alterations by TE-derived dual coding events were observed in 129 human genes. Comparative TE-derived dual coding events were also analyzed in chimpanzee and mouse orthologs. Seventy chimpanzee orthologs had TE-derived dual coding events, but mouse orthologs did not have any TE-derived dual coding events. Taken together, our analyses listed the number of TE-derived dual coding genes which could be investigated by experimental analysis and suggested that TE-derived dual coding events were major sources for the functional diversity of human genes, but not mouse genes.

Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery

MOTIVATION

Signaling pathways control a large variety of cellular processes. However, currently, even within the same database signaling pathways are often curated at different levels of detail. This makes comparative and cross-talk analyses difficult.

RESULTS

We present SignaLink, a database containing eight major signaling pathways from Caenorhabditis elegans, Drosophila melanogaster and humans. Based on 170 review and approximately 800 research articles, we have compiled pathways with semi-automatic searches and uniform, well-documented curation rules. We found that in humans any two of the eight pathways can cross-talk. We quantified the possible tissue- and cancer-specific activity of cross-talks and found pathway-specific expression profiles. In addition, we identified 327 proteins relevant for drug target discovery.

CONCLUSIONS

We provide a novel resource for comparative and cross-talk analyses of signaling pathways. The identified multi-pathway and tissue-specific cross-talks contribute to the understanding of the signaling complexity in health and disease, and underscore its importance in network-based drug target selection.

AVAILABILITY

http://SignaLink.org.

Human transcriptome nexuses: basic-eukaryotic and metazoan

Using a new approach, I analysed human transcriptome coexpression network and revealed two large-scale nexuses. Besides gene coexpression, each nexus is characterized by a combination of gene evolutionary origin, function and among-tissues expression breadth. The first nexus contains mostly genes of pre-metazoan origin, which are widely expressed and have cell-centred functions. The second nexus is enriched in genes of metazoan origin, which are expressed more narrowly and have organism-centred functions. The revealed nexuses are supported by asymmetry in distribution of transcription factor targets between them. Within the metazoan nexus, there is a subnexus that is more pronounced in the nervous tissues and is enriched in gene regulatory complexity. It mostly contains genes related to nervous system, cell communication and multicellular organism processes and development. The revealed nexuses indicate a dichotomy in the transcriptional regulation and can provide a framework for further functional genomics studies.

The restricted partition method

For many complex traits, the bulk of the phenotypic variation attributable to genetic factors remains unexplained, even after well-powered genome-wide association studies. Among the multiple possible explanations for the "missing" variance, joint effects of multiple genetic variants are a particularly appealing target for investigation: they are well documented in biology and can often be evaluated using existing data. The first two sections of this chapter discusses these and other concerns that led to the development of the Restricted Partition Method (RPM). The RPM is an exploratory tool designed to investigate, in a model agnostic manner, joint effects of genetic and environmental factors contributing to quantitative or dichotomous phenotypes. The method partitions multilocus genotypes (or genotype-environmental exposure classes) into statistically distinct "risk" groups, then evaluates the resulting model for phenotypic variance explained. It is sensitive to factors whose effects are apparent only in a joint analysis, and which would therefore be missed by many other methods. The third section of the chapter provides details of the RPM algorithm and walks the reader through an example. The final sections of the chapter discuss practical issues related to the use of the method. Because exhaustive pairwise or higher order analyses of many SNPs are computationally burdensome, much of the discussion focuses on computational issues. The RPM proved to be practical for a large candidate gene analysis, consisting of over 40,000 SNPs, using a desktop computer. Because the algorithm and software lend themselves to distributed processing, larger analyses can easily be split among multiple computers.

Positive selection of tyrosine loss in metazoan evolution

John Nash showed that within a complex system, individuals are best off if they make the best decision that they can, taking into account the decisions of the other individuals. Here, we investigate whether similar principles influence the evolution of signaling networks in multicellular animals. Specifically, by analyzing a set of metazoan species we observed a striking negative correlation of genomically encoded tyrosine content with biological complexity (as measured by the number of cell types in each organism). We discuss how this observed tyrosine loss correlates with the expansion of tyrosine kinases in the evolution of the metazoan lineage and how it may relate to the optimization of signaling systems in multicellular animals. We propose that this phenomenon illustrates genome-wide adaptive evolution to accommodate beneficial genetic perturbation.

Shallow Reductionism and the Problem of Complexity in Psychology

In his recent book The Mind Doesn't Work That Way, Fodor argues that computational modeling of global cognitive processes, such as abductive everyday reasoning, has not been successful. In this article the problem is analyzed in the framework of algorithmic information theory. It is argued that the failed approaches are characterized by shallow reductionism, which is rejected in favor of deep reductionism and nonreductionism.

Gene-centric genomewide association study via entropy

Genes are the functional units in most organisms. Compared to genetic variants located outside genes, genic variants are more likely to affect disease risk. The development of the human HapMap project provides an unprecedented opportunity for genetic association studies at the genomewide level for elucidating disease etiology. Currently, most association studies at the single-nucleotide polymorphism (SNP) or the haplotype level rely on the linkage information between SNP markers and disease variants, with which association findings are difficult to replicate. Moreover, variants in genes might not be sufficiently covered by currently available methods. In this article, we present a gene-centric approach via entropy statistics for a genomewide association study to identify disease genes. The new entropy-based approach considers genic variants within one gene simultaneously and is developed on the basis of a joint genotype distribution among genetic variants for an association test. A grouping algorithm based on a penalized entropy measure is proposed to reduce the dimension of the test statistic. Type I error rates and power of the entropy test are evaluated through extensive simulation studies. The results indicate that the entropy test has stable power under different disease models with a reasonable sample size. Compared to single SNP-based analysis, the gene-centric approach has greater power, especially when there is more than one disease variant in a gene. As the genomewide genic SNPs become available, our entropy-based gene-centric approach would provide a robust and computationally efficient way for gene-based genomewide association study.

A generative bias towards average complexity in artificial cell lineages

The evolution of life on earth has been characterized by generalized long-term increases in phenotypic complexity. Although natural selection is a plausible cause for these trends, one alternative hypothesis--generative bias--has been proposed repeatedly based on theoretical considerations. Here, we introduce a computational model of a developmental system and use it to test the hypothesis that long-term increasing trends in phenotypic complexity are caused by a generative bias towards greater complexity. We use our model to generate random organisms with different levels of phenotypic complexity and analyse the distributions of mutational effects on complexity. We show that highly complex organisms are easy to generate but there are trade-offs between different measures of complexity. We also find that only the simplest possible phenotypes show a generative bias towards higher complexity, whereas phenotypes with high complexity display a generative bias towards lower complexity. These results suggest that generative biases alone are not sufficient to explain long-term evolutionary increases in phenotypic complexity. Rather, our finding of a generative bias towards average complexity argues for a critical role of selective biases in driving increases in phenotypic complexity and in maintaining high complexity once it has evolved.

Transcriptomic identification of candidate genes involved in sunflower responses to chilling and salt stresses based on cDNA microarray analysis

BACKGROUND

Considering that sunflower production is expanding to arid regions, tolerance to abiotic stresses as drought, low temperatures and salinity arises as one of the main constrains nowadays. Differential organ-specific sunflower ESTs (expressed sequence tags) were previously generated by a subtractive hybridization method that included a considerable number of putative abiotic stress associated sequences. The objective of this work is to analyze concerted gene expression profiles of organ-specific ESTs by fluorescence microarray assay, in response to high sodium chloride concentration and chilling treatments with the aim to identify and follow up candidate genes for early responses to abiotic stress in sunflower.

RESULTS

Abiotic-related expressed genes were the target of this characterization through a gene expression analysis using an organ-specific cDNA fluorescence microarray approach in response to high salinity and low temperatures. The experiment included three independent replicates from leaf samples. We analyzed 317 unigenes previously isolated from differential organ-specific cDNA libraries from leaf, stem and flower at R1 and R4 developmental stage. A statistical analysis based on mean comparison by ANOVA and ordination by Principal Component Analysis allowed the detection of 80 candidate genes for either salinity and/or chilling stresses. Out of them, 50 genes were up or down regulated under both stresses, supporting common regulatory mechanisms and general responses to chilling and salinity. Interestingly 15 and 12 sequences were up regulated or down regulated specifically in one stress but not in the other, respectively. These genes are potentially involved in different regulatory mechanisms including transcription/translation/protein degradation/protein folding/ROS production or ROS-scavenging. Differential gene expression patterns were confirmed by qRT-PCR for 12.5% of the microarray candidate sequences.

CONCLUSION

Eighty genes isolated from organ-specific cDNA libraries were identified as candidate genes for sunflower early response to low temperatures and salinity. Microarray profiling of chilling and NaCl-treated sunflower leaves revealed dynamic changes in transcript abundance, including transcription factors, defense/stress related proteins, and effectors of homeostasis, all of which highlight the complexity of both stress responses. This study not only allowed the identification of common transcriptional changes to both stress conditions but also lead to the detection of stress-specific genes not previously reported in sunflower. This is the first organ-specific cDNA fluorescence microarray study addressing a simultaneous evaluation of concerted transcriptional changes in response to chilling and salinity stress in cultivated sunflower.

Organismal complexity, cell differentiation and gene expression: human over mouse

We present a molecular and cellular phenomenon underlying the intriguing increase in phenotypic organizational complexity. For the same set of human-mouse orthologous genes (11 534 gene pairs) and homologous tissues (32 tissue pairs), human shows a greater fraction of tissue-specific genes and a greater ratio of the total expression of tissue-specific genes to housekeeping genes in each studied tissue, which suggests a generally higher level of evolutionary cell differentiation (specialization). This phenomenon is spectacularly more pronounced in those human tissues that are more directly involved in the increase of complexity, longevity and body size (i.e. it is reflected on the organismal level as well). Genes with a change in expression breadth show a greater human-mouse divergence of promoter regions and encoded proteins (i.e. the functional genomics data are supported by the structural analysis). Human also shows the higher expression of translation machinery. The upstream untranslated regions (5'UTRs) of human mRNAs are longer than mouse 5'UTRs (even after correction for the difference in genome sizes) and contain more uAUG codons, which suggest a more complex regulation at the translational level in human cells (and agrees well with the augmented cell specialization).

A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence

The determination of gene-by-gene and gene-by-environment interactions has long been one of the greatest challenges in genetics. The traditional methods are typically inadequate because of the problem referred to as the "curse of dimensionality." Recent combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, the combinatorial partitioning method, and the restricted partition method, have a straightforward correspondence to the concept of the phenotypic landscape that unifies biological, statistical genetics, and evolutionary theories. However, the existing approaches have several limitations, such as not allowing for covariates, that restrict their practical use. In this study, we report a generalized MDR (GMDR) method that permits adjustment for discrete and quantitative covariates and is applicable to both dichotomous and continuous phenotypes in various population-based study designs. Computer simulations indicated that the GMDR method has superior performance in its ability to identify epistatic loci, compared with current methods in the literature. We applied our proposed method to a genetics study of four genes that were reported to be associated with nicotine dependence and found significant joint action between CHRNB4 and NTRK2. Moreover, our example illustrates that the newly proposed GMDR approach can increase prediction ability, suggesting that its use is justified in practice. In summary, GMDR serves the purpose of identifying contributors to population variation better than do the other existing methods.

Quantifying organismal complexity using a population genetic approach

BACKGROUND

Various definitions of biological complexity have been proposed: the number of genes, cell types, or metabolic processes within an organism. As knowledge of biological systems has increased, it has become apparent that these metrics are often incongruent.

METHODOLOGY

Here we propose an alternative complexity metric based on the number of genetically uncorrelated phenotypic traits contributing to an organism's fitness. This metric, phenotypic complexity, is more objective than previous suggestions, as complexity is measured from a fundamental biological perspective, that of natural selection. We utilize a model linking the equilibrium fitness (drift load) of a population to phenotypic complexity. We then use results from viral evolution experiments to compare the phenotypic complexities of two viruses, the bacteriophage X174 and vesicular stomatitis virus, and to illustrate the consistency of our approach and its applicability.

CONCLUSIONS/SIGNIFICANCE

Because Darwinian evolution through natural selection is the fundamental element unifying all biological organisms, we propose that our metric of complexity is potentially a more relevant metric than others, based on the count of artificially defined set of objects.

Usefulness of cancer-testis antigens as biomarkers for the diagnosis and treatment of hepatocellular carcinoma

Despite advances in our cellular and molecular knowledge, hepatocellular carcinoma (HCC) remains one of the major public health problems throughout the world. It is now known to be highly heterogeneous: it encompasses various pathological entities and a wide range of clinical behaviors, and is underpinned by a complex array of gene alterations that affect supra-molecular processes.Four families of HCC tumour markers have been recently proposed: a) onco-fetal and glycoprotein antigens; b) enzymes and iso-enzymes; c) cytokines and d) genes. A category of tumour-associated antigens called cancer-testis (CT) antigens has been identified and their encoding genes have been extensively investigated. CT antigens are expressed in a limited number of normal tissues as well as in malignant tumors of unrelated histological origin, including the liver. Given that cancers are being recognized as increasingly complex, we here review the role of CT antigens as liver tumour biomarkers and their validation process, and discuss why they may improve the effectiveness of screening HCC patients and help in determining the risk of developing HCC.

'Genome design' model and multicellular complexity: golden middle

Human tissue-specific genes were reported to be longer than housekeeping genes (both in coding and intronic parts). The competing neutralist and adaptationist models were proposed to explain this observation. Here I show that in human genome the longest are genes with the intermediate expression pattern. From the standpoint of information theory, the regulation of such genes should be most complex. In the genomewide context, they are found here to have the higher informational load on all available levels: from participation in protein interaction networks, pathways and modules reflected in Gene Ontology categories through transcription factor regulatory sets and protein functional domains to amino acid tuples (words) in encoded proteins and nucleotide tuples in introns and promoter regions. Thus, the intermediately expressed genes have the higher functional and regulatory complexity that is reflected in their greater length (which is consistent with the 'genome design' model). The dichotomy of housekeeping versus tissue-specific entities is more pronounced on the modular level than on the molecular level. There are much lesser intermediate-specific modules (modules overrepresented in the intermediately expressed genes) than housekeeping or tissue-specific modules (normalized to gene number). The dichotomy of housekeeping versus tissue-specific genes and modules in multicellular organisms is probably caused by the burden of regulatory complexity acted on the intermediately expressed genes.

Cancer initiation and progression: an unsimplifiable complexity

BACKGROUND

Cancer remains one of the most complex diseases affecting humans and, despite the impressive advances that have been made in molecular and cell biology, how cancer cells progress through carcinogenesis and acquire their metastatic ability is still widely debated.

CONCLUSION

There is no doubt that human carcinogenesis is a dynamic process that depends on a large number of variables and is regulated at multiple spatial and temporal scales. Viewing cancer as a system that is dynamically complex in time and space will, however, probably reveal more about its underlying behavioural characteristics. It is encouraging that mathematicians, biologists and clinicians continue to contribute together towards a common quantitative understanding of cancer complexity. This way of thinking may further help to clarify concepts, interpret new and old experimental data, indicate alternative experiments and categorize the acquired knowledge on the basis of the similarities and/or shared behaviours of very different tumours.

Protein interaction networks in plants

Protein-protein interactions are fundamental to virtually every aspect of cellular functions. With the development of high-throughput technologies of both the yeast two-hybrid system and tandem mass spectrometry, genome-wide protein-linkage mapping has become a major objective in post-genomic research. While at least partial "interactome" networks of several model organisms are already available, in the plant field, progress in this respect is slow. However, even with comprehensive protein interaction data still missing, substantial recent advance in the graph-theoretical functional interpretation of complex network architectures might pave the way for novel approaches in plant research. This article reviews current progress and discussions in network biology. Emphasis is put on the question of what can be learned about protein functions and cellular processes by studying the topology of complex protein interaction networks and the evolutionary mechanisms underlying their development. Particularly the intermediate and local levels of network organization--the modules, motifs and cliques--are increasingly recognized as the operational units of biological functions. As demonstrated by some recent results from systematic analyses of plant protein families, protein interaction networks promise to be a valuable tool for a molecular understanding of functional specificities and for identifying novel regulatory components and pathways.

Protein family expansions and biological complexity

During the course of evolution, new proteins are produced very largely as the result of gene duplication, divergence and, in many cases, combination. This means that proteins or protein domains belong to families or, in cases where their relationships can only be recognised on the basis of structure, superfamilies whose members descended from a common ancestor. The size of superfamilies can vary greatly. Also, during the course of evolution organisms of increasing complexity have arisen. In this paper we determine the identity of those superfamilies whose relative sizes in different organisms are highly correlated to the complexity of the organisms. As a measure of the complexity of 38 uni- and multicellular eukaryotes we took the number of different cell types of which they are composed. Of 1,219 superfamilies, there are 194 whose sizes in the 38 organisms are strongly correlated with the number of cell types in the organisms. We give outline descriptions of these superfamilies. Half are involved in extracellular processes or regulation and smaller proportions in other types of activity. Half of all superfamilies have no significant correlation with complexity. We also determined whether the expansions of large superfamilies correlate with each other. We found three large clusters of correlated expansions: one involves expansions in both vertebrates and plants, one just in vertebrates, and one just in plants. Our work identifies important protein families and provides one explanation of the discrepancy between the total number of genes and the apparent physiological complexity of eukaryotic organisms.

Cancer: looking for simplicity and finding complexity

Cancer is one of the most complex dynamic human disease. Despite rapid advances in the fields of molecular and cell biology, it is still widely debated as to how neoplastic cells progress through carcinogenesis and acquire their metastatic ability. The need to find a new way of observing anatomical entities and their underlying processes, and measuring the changes they undergo, prompted us to investigate the Theory of Complexity, and to apply its principles to human cancer. Viewing a neoplasm as a system that is complex in time and space it is likely to reveal more about its behavioral characteristics, and this manner of thinking may help to clarify concepts, interpret experimental data, indicate specific experiments and categorize the rich body of knowledge on the basis of the similarities and/or shared behaviors of very different tumors.

Estimating the tempo and mode of gene family evolution from comparative genomic data

Comparison of whole genomes has revealed that changes in the size of gene families among organisms is quite common. However, there are as yet no models of gene family evolution that make it possible to estimate ancestral states or to infer upon which lineages gene families have contracted or expanded. In addition, large differences in family size have generally been attributed to the effects of natural selection, without a strong statistical basis for these conclusions. Here we use a model of stochastic birth and death for gene family evolution and show that it can be efficiently applied to multispecies genome comparisons. This model takes into account the lengths of branches on phylogenetic trees, as well as duplication and deletion rates, and hence provides expectations for divergence in gene family size among lineages. The model offers both the opportunity to identify large-scale patterns in genome evolution and the ability to make stronger inferences regarding the role of natural selection in gene family expansion or contraction. We apply our method to data from the genomes of five yeast species to show its applicability.

Uncertainty principle of genetic information in a living cell

Background

Formal description of a cell's genetic information should provide the number of DNA molecules in that cell and their complete nucleotide sequences. We pose the formal problem: can the genome sequence forming the genotype of a given living cell be known with absolute certainty so that the cell's behaviour (phenotype) can be correlated to that genetic information? To answer this question, we propose a series of thought experiments.

Results

We show that the genome sequence of any actual living cell cannot physically be known with absolute certainty, independently of the method used. There is an associated uncertainty, in terms of base pairs, equal to or greater than μs (where μ is the mutation rate of the cell type and s is the cell's genome size).

Conclusion

This finding establishes an "uncertainty principle" in genetics for the first time, and its analogy with the Heisenberg uncertainty principle in physics is discussed. The genetic information that makes living cells work is thus better represented by a probabilistic model rather than as a completely defined object.